Lithium-ion battery is an appropriate choice for electric vehicle (EV) due to its promising features of high voltage, high energy density, low self-discharge and long lifecycles. The successful operation of EV is highly dependent on the operation of battery management system (BMS). State of charge (SOC) is one of the vital paraments of BMS which signifies the amount of charge left in a battery. A good estimation of SOC leads to long battery life and prevention of catastrophe from battery failure. Besides, an accurate and robust SOC estimation has great significance towards an efficient EV operation. However, SOC estimation is a complex process due to its dependency on various factors such as battery age, ambient temperature, and many unknown factors. This review presents the recent SOC estimation methods highlighting the model-based and data-driven approaches. Model-based methods attempt to model the battery behavior incorporating various factors into complex mathematical equations in order to accurately estimate the SOC while the data-driven methods adopt an approach of learning the battery’s behavior by running complex algorithms with a large amount of measured battery data. The classifications of model-based and data-driven based SOC estimation are explained in terms of estimation model/algorithm, benefits, drawbacks, and estimation error. In addition, the review highlights many factors and challenges and delivers potential recommendations for the development of SOC estimation methods in EV applications. All the highlighted insights of this review will hopefully lead to increased efforts toward the enhancement of SOC estimation method of lithium-ion battery for the future high-tech EV applications.

The state-of-charge (SOC) is a crucial parameter of a battery management system (BMS) for Li-ion batteries. The SOC indicates the amount of charge left in the battery of electric vehicles - akin to the fuel gauge in combustion vehicles. An accurate SOC knowledge contributes largely to the longevity, performance, reliability of the battery. However, the SOC of Li-ion batteries cannot be easily measured by any apparatus. Furthermore, the SOC also can be influenced by numerous incalculable factors such as battery chemistry, ambient environment, aging factor, etc. In this study, we propose a SOC estimation model for a Li-ion battery using an improved deep neural network (DNN) approach for electric vehicle applications. We found that a DNN with a sufficient number of hidden layers is capable of predicting the SOC of the unseen drive cycles during training. We developed a series of DNN models with a varying number of hidden layers, its training algorithm was to investigate their respective performance when evaluated on different drive cycles. We observe that increasing hidden layers in DNN (up to 4 hidden layers) decreases the error rate and improves the SOC estimation. An additional increase of hidden layer beyond that increases the error rate. In this study we show that a 4 hidden layer DNN trained on dynamic stress test (DST) drive cycle is capable of predicting SOC values unexpectedly well of other unseen drive cycles such as federal urban driving schedule (FUDS), Beijing dynamic stress test (BJDST), and supplemental federal test procedure (US06), respectively.